A Non-Parametric Dimension Test of the Term Structure

Published as an article in: Studies in Nonlinear Dynamics & Econometrics, 2004, vol. 8, issue 3, article 6.

Autorregresive conditional volatility, skewness and kurtosis

This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. The model is estimated assuming a Gram-Charlier series expansion of the normal density function for the error term, which is easier to estimate than the non-central t distribution proposed by Harvey and Siddique (1999). Moreover, this approach accounts for time-varying skewness and kurtosis while the approach by Harvey and Siddique (1999) only accounts for nonnormal skewness. We apply this method to daily returns of a variety of stock indices and exchange rates. Our results indicate a significant presence of conditional skewness and kurtosis. It is also found that specifications allowing for time-varying skewness and kurtosis outperform specifications with constant third and fourth moments.

An empirical comparison of the performance of alternative option pricing models

Published as an article in: Investigaciones Economicas, 2005, vol. 29, issue 3, pages 483-523.

GABAergic and Cortical and Subcortical Glutamatergic Axon Terminals Contain CB1 Cannabinoid Receptors in the Ventromedial Nucleus of the Hypothalamus

Background: Type-1 cannabinoid receptors (CB1R) are enriched in the hypothalamus, particularly in the ventromedial hypothalamic nucleus (VMH) that participates in homeostatic and behavioral functions including food intake. Although CB1R activation modulates excitatory and inhibitory synaptic transmission in the brain, CB1R contribution to the molecular architecture of the excitatory and inhibitory synaptic terminals in the VMH is not known. Therefore, the aim of this study was to investigate the precise subcellular distribution of CB1R in the VMH to better understand the modulation exerted by the endocannabinoid system on the complex brain circuitries converging into this nucleus. Methodology/Principal Findings: Light and electron microscopy techniques were used to analyze CB1R distribution in the VMH of CB1R-WT, CB1R-KO and conditional mutant mice bearing a selective deletion of CB1R in cortical glutamatergic (Glu-CB1R-KO) or GABAergic neurons (GABA-CB1R-KO). At light microscopy, CB1R immunolabeling was observed in the VMH of CB1R-WT and Glu-CB1R-KO animals, being remarkably reduced in GABA-CB1R-KO mice. In the electron microscope, CB1R appeared in membranes of both glutamatergic and GABAergic terminals/preterminals. There was no significant difference in the percentage of CB1R immunopositive profiles and CB1R density in terminals making asymmetric or symmetric synapses in CB1R-WT mice. Furthermore, the proportion of CB1R immunopositive terminals/preterminals in CB1R-WT and Glu-CB1R-KO mice was reduced in GABA-CB1R-KO mutants. CB1R density was similar in all animal conditions. Finally, the percentage of CB1R labeled boutons making asymmetric synapses slightly decreased in Glu-CB1R-KO mutants relative to CB1R-WT mice, indicating that CB1R was distributed in cortical and subcortical excitatory synaptic terminals. Conclusions/Significance: Our anatomical results support the idea that the VMH is a relevant hub candidate in the endocannabinoid-mediated modulation of the excitatory and inhibitory neurotransmission of cortical and subcortical pathways regulating essential hypothalamic functions for the individual's survival such as the feeding behavior.

Enhanced Operation of Electricity Distribution Grids Through Smart Metering PLC Network Monitoring, Analysis and Grid Conditioning

Low Voltage (LV) electricity distribution grid operations can be improved through a combination of new smart metering systems' capabilities based on real time Power Line Communications (PLC) and LV grid topology mapping. This paper presents two novel contributions. The first one is a new methodology developed for smart metering PLC network monitoring and analysis. It can be used to obtain relevant information from the grid, thus adding value to existing smart metering deployments and facilitating utility operational activities. A second contribution describes grid conditioning used to obtain LV feeder and phase identification of all connected smart electric meters. Real time availability of such information may help utilities with grid planning, fault location and a more accurate point of supply management.

Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.

Extending Distance-based Ranking Models In Estimation of Distribution Algorithms

Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking models, such as Mallows and Generalized Mallows under the Kendall’s-t distance, have demonstrated their validity when solving this type of problems. Nevertheless, there are still many trends that deserve further study. In this paper, we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam. In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall, Cayley and Ulam distances, we run them on a benchmark of 120 instances from four well known permutation problems. The conducted experiments showed that there is not just one metric that performs the best in all the problems. However, the statistical test pointed out that Mallows-Ulam EDA is the most stable algorithm among the studied proposals.

NEW METHODOLOGY FOR FEEDER RECONFIGURATION IN DISTRIBUTION NETWORKS WITH DISTRIBUTED ENERGY RESOURCES

This doctoral Thesis defines and develops a new methodology for feeder reconfiguration in distribution networks with Distributed Energy Resources (DER). The proposed methodology is based on metaheuristic Ant Colony Optimization (ACO) algorithms. The methodology is called Item Oriented Ant System (IOAS) and the doctoral Thesis also defines three variations of the original methodology, Item Oriented Ant Colony System (IOACS), Item Oriented Max-min Ant System (IOMMAS) y Item Oriented Max-min Ant Colony System (IOACS). All methodologies pursue a twofold objective, to minimize the power losses and maximize DER penetration in distribution networks. The aim of the variations is to find the algorithm that adapts better to the present optimization problem, solving it most efficiently. The main feature of the methodology lies in the fact that the heuristic information and the exploitation information (pheromone) are attached to the item not to the path. Besides, the doctoral Thesis proposes to use feeder reconfiguration in order to increase the distribution network capacity of accepting a major degree of DER. The proposed methodology and its three variations have been tested and verified in two distribution networks well documented in the existing bibliography. These networks have been modeled and used to test all proposed methodologies for different scenarios with various DER penetration degrees.

Unveiling the factors shaping the distribution of widely distributed alpine vertebrates, using multi-scale ecological niche modelling of the bat Plecotus macrobullaris

Several alpine vertebrates share a distribution pattern that extends across the South-western Palearctic but is limited to the main mountain massifs. Although they are usually regarded as cold-adapted species, the range of many alpine vertebrates also includes relatively warm areas, suggesting that factors beyond climatic conditions may be driving their distribution. In this work we first recognize the species belonging to the mentioned biogeographic group and, based on the environmental niche analysis of Plecotus macrobullaris, we identify and characterize the environmental factors constraining their ranges. Distribution overlap analysis of 504 European vertebrates was done using the Sorensen Similarity Index, and we identified four birds and one mammal that share the distribution with P. macrobullaris. We generated 135 environmental niche models including different variable combinations and regularization values for P. macrobullaris at two different scales and resolutions. After selecting the best models, we observed that topographic variables outperformed climatic predictors, and the abruptness of the landscape showed better predictive ability than elevation. The best explanatory climatic variable was mean summer temperature, which showed that P. macrobullaris is able to cope with mean temperature ranges spanning up to 16 degrees C. The models showed that the distribution of P. macrobullaris is mainly shaped by topographic factors that provide rock-abundant and open-space habitats rather than climatic determinants, and that the species is not a cold-adapted, but rather a cold-tolerant eurithermic organism. P. macrobullaris shares its distribution pattern as well as several ecological features with five other alpine vertebrates, suggesting that the conclusions obtained from this study might be extensible to them. We concluded that rock-dwelling and open-space foraging vertebrates with broad temperature tolerance are the best candidates to show wide alpine distribution in the Western Palearctic.

Seasonal Blowfly Distribution and Abundance in Fragmented Landscapes. Is It Useful in Forensic Inference about Where a Corpse Has Been Decaying?

Blowflies are insects of forensic interest as they may indicate characteristics of the environment where a body has been laying prior to the discovery. In order to estimate changes in community related to landscape and to assess if blowfly species can be used as indicators of the landscape where a corpse has been decaying, we studied the blowfly community and how it is affected by landscape in a 7,000 km(2) region during a whole year. Using baited traps deployed monthly we collected 28,507 individuals of 10 calliphorid species, 7 of them well represented and distributed in the study area. Multiple Analysis of Variance found changes in abundance between seasons in the 7 analyzed species, and changes related to land use in 4 of them (Calliphora vomitoria, Lucilia ampullacea, L. caesar and L. illustris). Generalised Linear Model analyses of abundance of these species compared with landscape descriptors at different scales found only a clear significant relationship between summer abundance of C. vomitoria and distance to urban areas and degree of urbanisation. This relationship explained more deviance when considering the landscape composition at larger geographical scales (up to 2,500 m around sampling site). For the other species, no clear relationship between land uses and abundance was found, and therefore observed changes in their abundance patterns could be the result of other variables, probably small changes in temperature. Our results suggest that blowfly community composition cannot be used to infer in what kind of landscape a corpse has decayed, at least in highly fragmented habitats, the only exception being the summer abundance of C. vomitoria.

Distribution of Healthcare resources II.Study and Comparison of a Linear Programming Problem and its Dual. Resolution and Evaluation of a Practical Case and its Programming using Excel and Win QSB.

[EN]This research had as primary objective to model different types of problems using linear programming and apply different methods so as to find an adequate solution to them. To achieve this objective, a linear programming problem and its dual were studied and compared. For that, linear programming techniques were provided and an introduction of the duality theory was given, analyzing the dual problem and the duality theorems. Then, a general economic interpretation was given and different optimal dual variables like shadow prices were studied through the next practical case: An aesthetic surgery hospital wanted to organize its monthly waiting list of four types of surgeries to maximize its daily income. To solve this practical case, we modelled the linear programming problem following the relationships between the primal problem and its dual. Additionally, we solved the dual problem graphically, and then we found the optimal solution of the practical case posed through its dual, following the different theorems of the duality theory. Moreover, how Complementary Slackness can help to solve linear programming problems was studied. To facilitate the solution Solver application of Excel and Win QSB programme were used.

Distribution theory and fundamental solutions of differential operators

The objective of this dissertation is to study the theory of distributions and some of its applications. Certain concepts which we would include in the theory of distributions nowadays have been widely used in several fields of mathematics and physics. It was Dirac who first introduced the delta function as we know it, in an attempt to keep a convenient notation in his works in quantum mechanics. Their work contributed to open a new path in mathematics, as new objects, similar to functions but not of their same nature, were being used systematically. Distributions are believed to have been first formally introduced by the Soviet mathematician Sergei Sobolev and by Laurent Schwartz. The aim of this project is to show how distribution theory can be used to obtain what we call fundamental solutions of partial differential equations.